User Authentication by Electroencephalographic Signals when Blinkin
Abstract
The article presents the results of a study in the field of applying electroencephalography (EEG) for human authentication. An algorithm for EEG authentication based on blinks has been developed and described. Authentication is carried out by one blink, which takes 2-5 seconds. The data is collected using a Muse electroencephalograph. Data preprocessing includes wavelet transform and blink detection. Geometric characteristics of the EEG signals are used as features. Recognition is conducted by the Random Forest classifier. According to the test results, the percentage of correct authentication was 95 %. There is the possibility of background authentication. The implemented system may be used to authenticate students at distant education.
References
Higher Education Opportunity, Act 122 Stat. 3078, Public Law 110–315, Aug. 14, 2008.
T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, “Impact of Artificial "Gummy"Fingers on Fingerprint Systems,” in Proc. of SPIE, Optical Security and Counterfeit Deterrence Techniques IV, vol. 4677, San Jose, CA, 2002, pp. 275–289; doi: 10.1117/12.462719
K. A. Nixon et al., “Spoof detection schemes,” in Handbook of Biometrics, A. K. Jain, P. Flynn, and A. A. Ross eds., Boston, MA: Springer, 2008, pp. 403–423; doi: 10.1007/978-0-387-71041-9_20
A. Anoklin et al., “A genetic study of the human low-voltage electroencephalogram,” Human Genetic, vol. 90, no. 1–2, pp. 99-112, 1992; doi: 10.1007/BF00210751
F. Vogel, “The genetic basis of the normal EEG,” Human Genetic, vol. 10, no. 2, pp. 91–114, 1970.
D. A. Timofeev and M. Yu. Maslov, “Architecture of data acquisition and processing system for improving productivity of software developers,” St. Petersburg State Polytechnical University Journal. Computer Science. Telecommunications and Control Systems, vol. 11, no. 4, pp. 71–81, 2018.
G. Mohammadi, P. Shoushtari, A. B. Molaee, and M. B. Shamsollahi, “Person Identification by Using AR Model for EEG Signals,” in Proc. of World Academy of Science Engineering and Technology, vol. 11, no. 2, pp. 281–285, 2006.
B. C. Armstrong et al., “Assessing the uniqueness, collectability, and permanence of a novel method for erp biometrics,” Neurocomputing, vol. 156, pp. 59–67, 2015; doi: 10.1016/j.neucom.2015.04.025
S.-K. Yeom, H.-I. Suk, and S.-W. Lee, “Person authentication from neural activity of face-specific visual self-representation,” Pattern Recognition, vol. 46, no. 4, pp. 1159–1169, 2013.
S. Marcel and J. D. R. Millan, “Person authentication using brainwaves (eeg) and maximum a posteriori model adaptation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 743–752, 2007; doi: 10.1109/TPAMI.2007.1012
C. Miyamoto, S. Baba, and I. Nakanishi, “Biometric person authentication using new spectral features of electroencephalogram (EEG),” in Proc. of the 2008 International Symposium on Intelligent Signal Processing and Communications Systems, Bangkok, Thailand, 2009; doi: 10.1109/ISPACS.2009.4806762
Z. Mu, J. Hu, and J. Min, “EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes,” Entropy, vol. 18, no. 12, 2016; doi: 10.3390/e18120432
M. Abo-Zahhad, S. M. Ahmed, and S. N. Abbas, “A new multi-level approach to eeg based human authentication using eye blinking,” Pattern Recognition. Lett., vol. 82, no. 2, 216–225, 2016; doi: 10.1016/j.patrec.2015.07.034
D. Denney and C. Denney, “The eye blink electro-oculogram,” Br. J. Ophthalmology, vol. 68, no. 4, pp. 225–228, 1984; doi: 10.1136/bjo.68.4.225
T. Ohya, Y. Nomoto, H. Koyama, and M. Kawasumi, “Research of Operation Method of Accessibility Equipment for Severely Handicapped Based on Voluntary Eye Blink,” in The 15th International Conference on Biomedical Engineering. IFMBE Proc., J. Goh ed., vol. 43, pp. 928–929, Springer, 2013
L. Breiman, “Random Forest,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
Cross-validation: evaluating estimator performance, in Scikit-learn. Machine Learning in Python. [Online]. Available: https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation
D. Beazley, “Python. Podrobnyi spravochnik” [Python. Essential Reference], Saint Petersburg, Russia: Simvol-Plyus, 2010. (in Russian).
“Jupyter-Notebook,” Real Python. [Online]. Available: https://realpython.com/jupyter-notebookintroduction/
O. E. Krigolsan et al., “Choosing MUSE validation of Low-Cost, Portable EEG System for ERP Research,” Frontiers in Neuroscience, vol. 11, p. 109, 2017; doi: 10.3389/fnins.2017.00109
Muse SDK. [Online]. Available: https://choosemuse.com/development/
PyMuse package. [Online]. Available: http://polycortex.polymtl.ca/
Scikit-learn. Machine Learning in Python. [Online]. Available: https://scikit-learn.org/stable/
This work is licensed under a Creative Commons Attribution 4.0 International License.
